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library(arules)
library(arulesViz)
# create a list of baskets
market_basket <-  
  list(  
    c("apple", "beer", "rice", "meat"),
    c("apple", "beer", "rice"),
    c("apple", "beer"), 
    c("apple", "pear"),
    c("milk", "beer", "rice", "meat"), 
    c("milk", "beer", "rice"), 
    c("milk", "beer"),
    c("milk", "pear")
  )

# set transaction names (T1 to T8)
#names(market_basket) <- paste("T", c(1:8), sep = "")

#trans <- as(market_basket, "transactions")
#dim(trans)
#itemLabels(trans)
#summary(trans)
#image(trans)
#itemFrequencyPlot(trans, topN=10,  cex.names=1)
#Min Support 0.3, confidence as 0.5.
rules <- apriori(trans, 
                 parameter = list(supp=0.3, conf=0.5, 
                                  maxlen=10, 
                                  target= "rules"))

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 2 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[6 item(s), 8 transaction(s)] done [0.00s].
sorting and recoding items ... [4 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 done [0.00s].
writing ... [10 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].

summary(rules)

set of 10 rules

rule length distribution (lhs + rhs):sizes
1 2 
4 6 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    1.0     1.0     2.0     1.6     2.0     2.0 

summary of quality measures:
    support        confidence        coverage           lift           count    
 Min.   :0.375   Min.   :0.5000   Min.   :0.5000   Min.   :1.000   Min.   :3.0  
 1st Qu.:0.375   1st Qu.:0.5000   1st Qu.:0.5625   1st Qu.:1.000   1st Qu.:3.0  
 Median :0.500   Median :0.5833   Median :0.7500   Median :1.000   Median :4.0  
 Mean   :0.475   Mean   :0.6417   Mean   :0.7750   Mean   :1.067   Mean   :3.8  
 3rd Qu.:0.500   3rd Qu.:0.7500   3rd Qu.:1.0000   3rd Qu.:1.000   3rd Qu.:4.0  
 Max.   :0.750   Max.   :1.0000   Max.   :1.0000   Max.   :1.333   Max.   :6.0  

mining info:

inspect(rules)

plot(rules, engine = "plotly")

To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.

plot(rules, measure = "confidence")

To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.

subrules <- head(rules, n = 10, by = "confidence")

plot(subrules, method = "graph",  engine = "htmlwidget")

NA

## Example 1: Create transaction data and mine association rules
a_list <- list(
      c("a","b","c"),
      c("a","b"),
      c("a","b","d"),
      c("c","e"),
      c("a","b","d","e")
      )

## Set transaction names
names(a_list) <- paste("Tr",c(1:5), sep = "")
a_list

$Tr1
[1] "a" "b" "c"

$Tr2
[1] "a" "b"

$Tr3
[1] "a" "b" "d"

$Tr4
[1] "c" "e"

$Tr5
[1] "a" "b" "d" "e"

## Use the constructor to create transactions
trans1 <- transactions(a_list)
trans1

transactions in sparse format with
 5 transactions (rows) and
 5 items (columns)

rules <- apriori(trans1)

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 0 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[5 item(s), 5 transaction(s)] done [0.00s].
sorting and recoding items ... [5 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 done [0.00s].
writing ... [19 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].

inspect(rules)

## Example 2: Mine association rules from an existing transactions dataset
##   using different minimum support and minimum confidence thresholds
data("Adult")

rules <- apriori(Adult,
    parameter = list(supp = 0.5, conf = 0.9, target = "rules"))

Apriori

Parameter specification:

Algorithmic control:

Absolute minimum support count: 24421 

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[115 item(s), 48842 transaction(s)] done [0.03s].
sorting and recoding items ... [9 item(s)] done [0.00s].
creating transaction tree ... done [0.02s].
checking subsets of size 1 2 3 4 done [0.00s].
writing ... [52 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].

summary(rules)

set of 52 rules

rule length distribution (lhs + rhs):sizes
 1  2  3  4 
 2 13 24 13 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   2.923   3.250   4.000 

summary of quality measures:
    support         confidence        coverage           lift            count      
 Min.   :0.5084   Min.   :0.9031   Min.   :0.5406   Min.   :0.9844   Min.   :24832  
 1st Qu.:0.5415   1st Qu.:0.9155   1st Qu.:0.5875   1st Qu.:0.9937   1st Qu.:26447  
 Median :0.5974   Median :0.9229   Median :0.6293   Median :0.9997   Median :29178  
 Mean   :0.6436   Mean   :0.9308   Mean   :0.6915   Mean   :1.0036   Mean   :31433  
 3rd Qu.:0.7426   3rd Qu.:0.9494   3rd Qu.:0.7945   3rd Qu.:1.0057   3rd Qu.:36269  
 Max.   :0.9533   Max.   :0.9583   Max.   :1.0000   Max.   :1.0586   Max.   :46560  

mining info:

plot(rules,engine="plotly")

plot(rules, method = "graph",  engine = "htmlwidget")